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plot.py
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plot.py
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import time
import bisect
import numpy as np
import pandas as pd
import networkx as nx
import scipy
import scipy.optimize
from scipy.interpolate import interp1d
import scipy as sp
import random as rd
import os, math
from datetime import datetime
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.dates import date2num, num2date
from lib.measures import (MeasureList, BetaMultiplierMeasure,
SocialDistancingForAllMeasure, BetaMultiplierMeasureByType,
SocialDistancingForPositiveMeasure, SocialDistancingByAgeMeasure, SocialDistancingForSmartTracing, ComplianceForAllMeasure)
from lib.rt import compute_daily_rts, R_T_RANGE
import numpy as np
import seaborn as sns
from matplotlib.colors import ListedColormap
DPI = 200
NO_PLOT = False
matplotlib.rcParams.update({
"figure.autolayout": False,
"figure.figsize": (6, 4),
"figure.dpi": 150,
"axes.linewidth": 0.8,
"xtick.major.width": 0.8,
"xtick.minor.width": 0.8,
"ytick.major.width": 0.8,
"ytick.minor.width": 0.8,
"text.usetex": True,
"font.family": "serif", # use serif rather than sans-serif
"font.serif": "Times New Roman", # use "Times New Roman" as the standard font
"font.size": 16,
"axes.titlesize": 16,
"axes.labelsize": 16,
"legend.fontsize": 14,
"legend.frameon": True,
"xtick.labelsize": 14,
"ytick.labelsize": 14,
"lines.linewidth": 2.0,
"lines.markersize": 4,
"grid.linewidth": 0.4,
})
def days_to_datetime(arr, start_date):
# timestamps
ts = arr * 24 * 60 * 60 + pd.Timestamp(start_date).timestamp()
return pd.to_datetime(ts, unit='s')
def lockdown_widget(lockdown_at, start_date, lockdown_label_y, ymax,
lockdown_label, ax, ls='--', xshift=0.0, zorder=None):
# Convert x-axis into posix timestamps and use pandas to plot as dates
lckdn_x = days_to_datetime(lockdown_at, start_date=start_date)
ax.plot([lckdn_x, lckdn_x], [0, ymax], linewidth=2.5, linestyle=ls,
color='black', label='_nolegend_', zorder=zorder)
lockdown_label_y = lockdown_label_y or ymax*0.4
ax.text(x=lckdn_x - pd.Timedelta(2.1 + xshift, unit='d'),
y=lockdown_label_y, s=lockdown_label, rotation=90)
def target_widget(show_target,start_date, ax, zorder=None):
txx = np.linspace(0, show_target.shape[0] - 1, num=show_target.shape[0])
txx = days_to_datetime(txx, start_date=start_date)
ax.plot(txx, show_target, linewidth=4, linestyle='', marker='X', ms=6,
color='red', label='COVID-19 case data', zorder=zorder)
class Plotter(object):
"""
Plotting class
"""
def __init__(self):
# plot constants
# check out https://colorhunt.co/
self.color_expo = '#ffcc00'
self.color_iasy = '#00a8cc'
self.color_ipre = '#005082'
self.color_isym = '#000839'
self.color_testing = '#ffa41b'
self.color_posi = '#21bf73'
self.color_nega = '#fd5e53'
self.color_all = '#ffa41b'
self.color_positive = '#00a8cc'
self.color_age = '#005082'
self.color_tracing = '#000839'
self.color_infected = '#000839'
self.filling_alpha = 0.5
self.color_different_scenarios = [
'#dc2ade',
'#21ff53',
'#323edd',
'#ff9021',
'#4d089a',
'#cc0066',
'#ff6666',
'#216353',
'#66cccc',
'#ff2222'
]
self.color_different_scenarios_alt = [
'#a1dab4',
'#41b6c4',
'#2c7fb8',
'#253494',
]
# sequential
# self.color_different_scenarios = [
# # '#ffffcc',
# '#c7e9b4',
# '#7fcdbb',
# '#41b6c4',
# '#2c7fb8',
# '#253494',
# '#000000'
# ]
# 2D visualization
self.density_alpha = 0.7
self.marker_home = "^"
self.marker_site = "o"
self.color_home = '#000839'
self.color_site = '#000000'
self.size_home = 80
self.size_site = 300
def __is_state_at(self, sim, r, state, t):
return (sim.state_started_at[state][r] <= t) & (sim.state_ended_at[state][r] > t)
def __state_started_before(self, sim, r, state, t):
return (sim.state_started_at[state][r] <= t)
def __is_contained_at(self, sim, r, measure, t):
contained = np.zeros(sim.n_people, dtype='bool')
for i in range(sim.n_people):
if measure == 'SocialDistancingForAllMeasure':
contained[i] = sim.measure_list[r].is_contained_prob(SocialDistancingForAllMeasure, t=t, j=i)
elif measure == 'SocialDistancingForSmartTracing':
contained[i] = sim.measure_list[r].is_contained_prob(SocialDistancingForSmartTracing, t=t, j=i)
elif measure == 'SocialDistancingByAgeMeasure':
contained[i] = sim.measure_list[r].is_contained_prob(SocialDistancingByAgeMeasure, t=t, age=sim.people_age[r, i])
elif measure == 'SocialDistancingForPositiveMeasure':
contained[i] = sim.measure_list[r].is_contained_prob(SocialDistancingForPositiveMeasure,
t=t, j=i,
state_posi_started_at=sim.state_started_at['posi'][r, :],
state_posi_ended_at=sim.state_ended_at['posi'][r, :],
state_resi_started_at=sim.state_started_at['resi'][r, :],
state_dead_started_at=sim.state_started_at['dead'][r, :])
else:
raise ValueError('Social distancing measure unknown.')
return contained
def __comp_state_cumulative(self, sim, state, acc):
'''
Computes `state` variable over time [0, self.max_time] with given accuracy `acc
'''
ts, means, stds = [], [], []
for t in np.linspace(0.0, sim.max_time, num=acc, endpoint=True):
restarts = [np.sum(self.__state_started_before(sim, r, state, t))
for r in range(sim.random_repeats)]
ts.append(t/24.0)
means.append(np.mean(restarts))
stds.append(np.std(restarts))
return np.array(ts), np.array(means), np.array(stds)
def __comp_state_over_time(self, sim, state, acc):
'''
Computes `state` variable over time [0, self.max_time] with given accuracy `acc
'''
ts, means, stds = [], [], []
for t in np.linspace(0.0, sim.max_time, num=acc, endpoint=True):
restarts = [np.sum(self.__is_state_at(sim, r, state, t))
for r in range(sim.random_repeats)]
ts.append(t/24.0)
means.append(np.mean(restarts))
stds.append(np.std(restarts))
return np.array(ts), np.array(means), np.array(stds)
def __comp_contained_over_time(self, sim, measure, acc):
'''
Computes `state` variable over time [0, self.max_time] with given accuracy `acc
'''
ts, means, stds = [], [], []
for t in np.linspace(0.0, sim.max_time, num=acc, endpoint=True):
restarts = [np.sum(self.__is_contained_at(sim, r, measure, t))
for r in range(sim.random_repeats)]
ts.append(t/24.0)
means.append(np.mean(restarts))
stds.append(np.std(restarts))
return np.array(ts), np.array(means), np.array(stds)
def plot_cumulative_infected(self, sim, title='Example', filename='daily_inf_0',
figsize=(6, 5), errorevery=20, acc=1000, ymax=None,
lockdown_label='Lockdown', lockdown_at=None,
lockdown_label_y=None, show_target=None,
test_lag=2, start_date='1970-01-01',
subplot_adjust=None, legend_loc='upper right'):
''''
Plots daily infected split by group
averaged over random restarts, using error bars for std-dev
'''
if acc > sim.max_time:
acc = int(sim.max_time)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
ts, iasy_mu, iasy_sig = self.__comp_state_cumulative(sim, 'iasy', acc)
# _, ipre_mu, ipre_sig = self.__comp_state_cumulative(sim, 'ipre', acc)
_, isym_mu, isym_sig = self.__comp_state_cumulative(sim, 'isym', acc)
# _, expo_mu, iexpo_sig = self.__comp_state_cumulative(sim, 'expo', acc)
# _, posi_mu, posi_sig = self.__comp_state_cumulative(sim, 'posi', acc)
line_xaxis = np.zeros(ts.shape)
line_iasy = iasy_mu
line_isym = iasy_mu + isym_mu
error_isym = np.sqrt(iasy_sig**2 + isym_sig**2)
# Convert x-axis into posix timestamps and use pandas to plot as dates
ts = days_to_datetime(ts, start_date=start_date)
# lines
ax.plot(ts, line_iasy, c='black', linestyle='-')
ax.errorbar(ts, line_isym, yerr=error_isym, c='black', linestyle='-',
elinewidth=0.8, errorevery=errorevery, capsize=3.0)
# filling
ax.fill_between(ts, line_xaxis, line_iasy, alpha=self.filling_alpha, label='Asymptomatic',
edgecolor=self.color_iasy, facecolor=self.color_iasy, linewidth=0, zorder=0)
ax.fill_between(ts, line_iasy, line_isym, alpha=self.filling_alpha, label='Symptomatic',
edgecolor=self.color_isym, facecolor=self.color_isym, linewidth=0, zorder=0)
# limits
if ymax is None:
ymax = 1.5 * np.max(iasy_mu + isym_mu)
ax.set_ylim((0, ymax))
# ax.set_xlabel('Days')
ax.set_ylabel('People')
# extra
if lockdown_at is not None:
lockdown_widget(lockdown_at, start_date,
lockdown_label_y, ymax,
lockdown_label, ax)
if show_target is not None:
target_widget(show_target, start_date, ax)
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
#set ticks every week
ax.xaxis.set_major_locator(mdates.WeekdayLocator())
#set major ticks format
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
fig.autofmt_xdate(bottom=0.2, rotation=0, ha='center')
# legend
ax.legend(loc=legend_loc, borderaxespad=0.5)
subplot_adjust = subplot_adjust or {'bottom':0.14, 'top': 0.98, 'left': 0.12, 'right': 0.96}
plt.subplots_adjust(**subplot_adjust)
plt.draw()
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def plot_daily_infected(self, sim, title='Example', filename='daily_inf_0',
figsize=(6, 5), errorevery=20, acc=1000, ymax=None,
lockdown_label='Lockdown', lockdown_at=None,
lockdown_label_y=None, show_target=None,
lockdown_end=None,
test_lag=2, start_date='1970-01-01',
subplot_adjust=None, legend_loc='upper right'):
''''
Plots daily infected split by group
averaged over random restarts, using error bars for std-dev
'''
if acc > sim.max_time:
acc = int(sim.max_time)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
ts, iasy_mu, iasy_sig = self.__comp_state_over_time(sim, 'iasy', acc)
_, ipre_mu, ipre_sig = self.__comp_state_over_time(sim, 'ipre', acc)
_, isym_mu, isym_sig = self.__comp_state_over_time(sim, 'isym', acc)
# _, expo_mu, iexpo_sig = self.__comp_state_over_time(sim, 'expo', acc)
# _, posi_mu, posi_sig = self.__comp_state_over_time(sim, 'posi', acc)
line_xaxis = np.zeros(ts.shape)
line_iasy = iasy_mu
line_ipre = iasy_mu + ipre_mu
line_isym = iasy_mu + ipre_mu + isym_mu
error_isym = np.sqrt(iasy_sig**2 + ipre_sig**2 + isym_sig**2)
# Convert x-axis into posix timestamps and use pandas to plot as dates
ts = days_to_datetime(ts, start_date=start_date)
# lines
ax.plot(ts, line_iasy,
c='black', linestyle='-')
ax.plot(ts, line_ipre,
c='black', linestyle='-')
ax.errorbar(ts, line_isym, yerr=error_isym, c='black', linestyle='-',
elinewidth=0.8, errorevery=errorevery, capsize=3.0)
# filling
ax.fill_between(ts, line_xaxis, line_iasy, alpha=self.filling_alpha, label='Asymptomatic',
edgecolor=self.color_iasy, facecolor=self.color_iasy, linewidth=0, zorder=0)
ax.fill_between(ts, line_iasy, line_ipre, alpha=self.filling_alpha, label='Pre-symptomatic',
edgecolor=self.color_ipre, facecolor=self.color_ipre, linewidth=0, zorder=0)
ax.fill_between(ts, line_ipre, line_isym, alpha=self.filling_alpha, label='Symptomatic',
edgecolor=self.color_isym, facecolor=self.color_isym, linewidth=0, zorder=0)
# limits
if ymax is None:
ymax = 1.5 * np.max(iasy_mu + ipre_mu + isym_mu)
ax.set_ylim((0, ymax))
# ax.set_xlabel('Days')
ax.set_ylabel('People')
# extra
if lockdown_at is not None:
lockdown_widget(lockdown_at, start_date,
lockdown_label_y, ymax,
lockdown_label, ax)
if lockdown_end is not None:
lockdown_widget(lockdown_at=lockdown_end, start_date=start_date,
lockdown_label_y=lockdown_label_y, ymax=ymax,
lockdown_label='End of lockdown', ax=ax, ls='dotted')
if show_target is not None:
target_widget(show_target, start_date, ax)
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
#set ticks every week
ax.xaxis.set_major_locator(mdates.WeekdayLocator())
#set major ticks format
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
fig.autofmt_xdate(bottom=0.2, rotation=0, ha='center')
# legend
ax.legend(loc=legend_loc, borderaxespad=0.5)
subplot_adjust = subplot_adjust or {'bottom':0.14, 'top': 0.98, 'left': 0.12, 'right': 0.96}
plt.subplots_adjust(**subplot_adjust)
plt.draw()
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def plot_daily_tested(self, sim, title='Example', filename='daily_tested_0', figsize=(10, 10), errorevery=20,
acc=1000, ymax=None, test_lag=2):
''''
Plots daily tested, positive daily tested, negative daily tested
averaged over random restarts, using error bars for std-dev
'''
if acc > sim.max_time:
acc = int(sim.max_time)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
ts, posi_mu, posi_sig = self.__comp_state_over_time(sim, 'posi', acc)
_, nega_mu, nega_sig = self.__comp_state_over_time(sim, 'nega', acc)
line_xaxis = np.zeros(ts.shape)
line_posi = posi_mu
line_nega = posi_mu + nega_mu
error_posi = posi_sig
error_nega = nega_sig + posi_sig
# shift by `test_lag` to count the cases on the real dates, as the real data does
T = posi_mu.shape[0]
corr_posi, corr_sig_posi = np.zeros(T - test_lag), np.zeros(T - test_lag)
corr_nega, corr_sig_nega = np.zeros(T - test_lag), np.zeros(T - test_lag)
corr_posi[0 : T - test_lag] = posi_mu[test_lag : T]
corr_sig_posi[0: T - test_lag] = posi_sig[test_lag: T]
corr_nega[0 : T - test_lag] = nega_mu[test_lag : T]
corr_sig_nega[0: T - test_lag] = nega_sig[test_lag: T]
# lines
ax.errorbar(ts[0 : T - test_lag], corr_posi, yerr=corr_sig_posi, elinewidth=0.8, errorevery=errorevery,
c='black', linestyle='-')
ax.errorbar(ts[0: T - test_lag], corr_nega, yerr=corr_sig_nega, elinewidth=0.8, errorevery=errorevery,
c='black', linestyle='-')
# filling
ax.fill_between(ts[0: T - test_lag], line_xaxis[0: T - test_lag], corr_posi, alpha=self.filling_alpha, label=r'Positive tests',
edgecolor=self.color_posi, facecolor=self.color_posi, linewidth=0, zorder=0)
ax.fill_between(ts[0: T - test_lag], corr_posi, corr_nega, alpha=self.filling_alpha, label=r'Negative tests',
edgecolor=self.color_nega, facecolor=self.color_nega, linewidth=0, zorder=0)
# axis
ax.set_xlim((0, np.max(ts)))
if ymax is None:
ymax = 1.5 * np.max(posi_mu + nega_mu)
ax.set_ylim((0, ymax))
ax.set_xlabel(r'$t$ [days]')
ax.set_ylabel(r'[people]')
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
# legend
fig.legend(loc='center right', borderaxespad=0.1)
# Adjust the scaling factor to fit your legend text completely outside the plot
plt.subplots_adjust(right=0.70)
ax.set_title(title, pad=20)
plt.draw()
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def plot_daily_at_home(self, sim, title='Example', filename='daily_at_home_0', figsize=(10, 10), errorevery=20, acc=1000, ymax=None):
''''
Plots daily tested, positive daily tested, negative daily tested
averaged over random restarts, using error bars for std-dev
'''
if acc > sim.max_time:
acc = int(sim.max_time)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
ts, all_mu, all_sig = self.__comp_contained_over_time(sim, 'SocialDistancingForAllMeasure', acc)
_, positive_mu, positive_sig = self.__comp_contained_over_time(sim, 'SocialDistancingForPositiveMeasure', acc)
_, age_mu, age_sig = self.__comp_contained_over_time(sim, 'SocialDistancingByAgeMeasure', acc)
_, tracing_mu, tracing_sig = self.__comp_contained_over_time(sim, 'SocialDistancingForSmartTracing', acc)
_, iasy_mu, iasy_sig = self.__comp_state_over_time(sim, 'iasy', acc)
_, ipre_mu, ipre_sig = self.__comp_state_over_time(sim, 'ipre', acc)
_, isym_mu, isym_sig = self.__comp_state_over_time(sim, 'isym', acc)
line_xaxis = np.zeros(ts.shape)
line_all = all_mu
line_positive = positive_mu
line_age = age_mu
line_tracing = tracing_mu
line_infected = iasy_mu + ipre_mu + isym_mu
error_all = all_sig
error_positive = positive_sig
error_age = age_sig
error_tracing = tracing_sig
error_infected = np.sqrt(np.square(iasy_sig) + np.square(ipre_sig) + np.square(isym_sig))
# lines
ax.errorbar(ts, line_infected, label=r'Total infected', errorevery=errorevery, c=self.color_infected, linestyle='--', yerr=error_infected)
ax.errorbar(ts, line_all, yerr=error_all, elinewidth=0.8, errorevery=errorevery,
c='black', linestyle='-')
ax.errorbar(ts, line_positive, yerr=error_positive, elinewidth=0.8, errorevery=errorevery,
c='black', linestyle='-')
ax.errorbar(ts, line_age, yerr=error_age, elinewidth=0.8, errorevery=errorevery,
c='black', linestyle='-')
ax.errorbar(ts, line_tracing, yerr=error_tracing, elinewidth=0.8, errorevery=errorevery,
c='black', linestyle='-')
# filling
ax.fill_between(ts, line_xaxis, line_all, alpha=self.filling_alpha, label=r'SD for all',
edgecolor=self.color_all, facecolor=self.color_all, linewidth=0, zorder=0)
ax.fill_between(ts, line_xaxis, line_positive, alpha=self.filling_alpha, label=r'SD for positively tested',
edgecolor=self.color_positive, facecolor=self.color_positive, linewidth=0, zorder=0)
ax.fill_between(ts, line_xaxis, line_age, alpha=self.filling_alpha, label=r'SD for age group',
edgecolor=self.color_age, facecolor=self.color_age, linewidth=0, zorder=0)
ax.fill_between(ts, line_xaxis, line_tracing, alpha=self.filling_alpha, label=r'SD for traced contacts',
edgecolor=self.color_tracing, facecolor=self.color_tracing, linewidth=0, zorder=0)
# axis
ax.set_xlim((0, np.max(ts)))
if ymax is None:
ymax = 1.5 * np.max([all_mu, positive_mu, age_mu, tracing_mu])
ax.set_ylim((0, ymax))
ax.set_xlabel(r'$t$ [days]')
ax.set_ylabel(r'[people]')
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
# legend
fig.legend(loc='center right', borderaxespad=0.1)
# Adjust the scaling factor to fit your legend text completely outside the plot
plt.subplots_adjust(right=0.70)
ax.set_title(title, pad=20)
plt.draw()
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def compare_total_infections(self, sims, titles, figtitle='Title',
filename='compare_inf_0', figsize=(10, 10), errorevery=20, acc=1000, ymax=None,
lockdown_label='Lockdown', lockdown_at=None, lockdown_label_y=None,
show_positives=False, test_lag=2, show_legend=True, legendYoffset=0.0, legend_is_left=False,
subplot_adjust=None, start_date='1970-01-01', first_one_dashed=False):
''''
Plots total infections for each simulation, named as provided by `titles`
to compare different measures/interventions taken. Colors taken as defined in __init__, and
averaged over random restarts, using error bars for std-dev
'''
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
for i in range(len(sims)):
if acc > sims[i].max_time:
acc = sims[i].max_time
ts, iasy_mu, iasy_sig = self.__comp_state_over_time(sims[i], 'iasy', acc)
_, ipre_mu, ipre_sig = self.__comp_state_over_time(sims[i], 'ipre', acc)
_, isym_mu, isym_sig = self.__comp_state_over_time(sims[i], 'isym', acc)
_, posi_mu, posi_sig = self.__comp_state_over_time(sims[i], 'posi', acc)
# Convert x-axis into posix timestamps and use pandas to plot as dates
ts = days_to_datetime(ts, start_date=start_date)
line_xaxis = np.zeros(ts.shape)
line_infected = iasy_mu + ipre_mu + isym_mu
error_infected = np.sqrt(np.square(iasy_sig) + np.square(ipre_sig) + np.square(isym_sig))
# lines
if show_positives:
ax.errorbar(ts, line_infected, yerr=error_infected, label='[Infected] ' + titles[i], errorevery=errorevery,
c=self.color_different_scenarios[i], linestyle='-')
# shift by `test_lag` to count the cases on the real dates, as the real data does
T = posi_mu.shape[0]
corr_posi, corr_sig = np.zeros(T - test_lag), np.zeros(T - test_lag)
corr_posi[0 : T - test_lag] = posi_mu[test_lag : T]
corr_sig[0: T - test_lag] = posi_sig[test_lag: T]
ax.errorbar(ts[:T - test_lag], corr_posi, yerr=corr_sig, label='[Tested positive]', errorevery=errorevery,
c=self.color_different_scenarios[i], linestyle='--', elinewidth=0.8)
else:
ax.errorbar(ts, line_infected, yerr=error_infected, label=titles[i], errorevery=errorevery, elinewidth=0.8,
capsize=3.0, c=self.color_different_scenarios[i], linestyle='--' if i == 0 and first_one_dashed else '-')
# filling
# ax.fill_between(ts, line_xaxis, line_infected, alpha=self.filling_alpha, zorder=0,
# edgecolor=self.color_different_scenarios[i], facecolor=self.color_different_scenarios[i], linewidth=0)
# axis
# ax.set_xlim((0, np.max(ts)))
if ymax is None:
ymax = 1.5 * np.max(iasy_mu + ipre_mu + isym_mu)
ax.set_ylim((0, ymax))
# ax.set_xlabel('Days')
ax.set_ylabel('People')
if lockdown_at is not None:
lockdown_widget(lockdown_at, start_date,
lockdown_label_y, ymax,
lockdown_label, ax, xshift=0.5)
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
#set ticks every week
# ax.xaxis.set_major_locator(mdates.WeekdayLocator())
ax.xaxis.set_major_locator(mdates.WeekdayLocator(interval=2))
#set major ticks format
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
fig.autofmt_xdate(bottom=0.2, rotation=0, ha='center')
if show_legend:
# legend
if legend_is_left:
leg = ax.legend(loc='upper left', borderaxespad=0.5)
else:
leg = ax.legend(loc='upper right', borderaxespad=0.5)
if legendYoffset != 0.0:
# Get the bounding box of the original legend
bb = leg.get_bbox_to_anchor().inverse_transformed(ax.transAxes)
# Change to location of the legend.
bb.y0 += legendYoffset
bb.y1 += legendYoffset
leg.set_bbox_to_anchor(bb, transform = ax.transAxes)
subplot_adjust = subplot_adjust or {'bottom':0.14, 'top': 0.98, 'left': 0.12, 'right': 0.96}
plt.subplots_adjust(**subplot_adjust)
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def compare_total_fatalities_and_hospitalizations(self, sims, titles, figtitle=r'Hospitalizations and Fatalities',
filename='compare_inf_0', figsize=(10, 10), errorevery=20, acc=1000, ymax=None, lockdown_at=None,
subplot_adjust=None, start_date='1970-01-01', first_one_dashed=False):
''''
Plots total fatalities and hospitalizations for each simulation, named as provided by `titles`
to compare different measures/interventions taken. Colors taken as defined in __init__, and
averaged over random restarts, using error bars for std-dev
'''
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
# hospitalizations
for i in range(len(sims)):
if acc > sims[i].max_time:
acc = sims[i].max_time
ts, hosp_mu, hosp_sig = self.__comp_state_over_time(
sims[i], 'hosp', acc)
ts, dead_mu, dead_sig = self.__comp_state_over_time(
sims[i], 'dead', acc)
# Convert x-axis into posix timestamps and use pandas to plot as dates
ts = days_to_datetime(ts, start_date=start_date)
# lines
ax.errorbar(ts, hosp_mu, yerr=hosp_sig, label=titles[i], errorevery=errorevery,
c=self.color_different_scenarios[i], linestyle='-', elinewidth=0.8, capsize=3.0)
ax.errorbar(ts, dead_mu, yerr=dead_sig, errorevery=errorevery,
c=self.color_different_scenarios[i], linestyle='--', elinewidth=0.8, capsize=3.0)
# axis
if ymax is None:
ymax = 1.5 * np.max(iasy_mu + ipre_mu + isym_mu)
ax.set_ylim((0, ymax))
# ax.set_xlabel('Days')
ax.set_ylabel('People')
if lockdown_at is not None:
ax.plot(lockdown_at * np.ones(acc), np.linspace(0, ymax, num=acc),
linewidth=1, linestyle='--', color='black', zorder=10)
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
#set ticks every week
# ax.xaxis.set_major_locator(mdates.WeekdayLocator())
ax.xaxis.set_major_locator(mdates.WeekdayLocator(interval=2))
#set major ticks format
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
fig.autofmt_xdate(bottom=0.2, rotation=0, ha='center')
# legend
# ax.legend(loc='upper right', borderaxespad=0.5)
ax.legend(loc='upper left', borderaxespad=0.5)
subplot_adjust = subplot_adjust or {
'bottom': 0.14, 'top': 0.98, 'left': 0.12, 'right': 0.96}
plt.subplots_adjust(**subplot_adjust)
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def plot_2d_infections_at_time(self, sim, at_time, density_bandwidth=1.0, restart=0,
title='Example', filename='2d_inf_0', figsize=(10, 10), acc=1000, ymax=None):
'''
Plots 2d visualization using mobility object. The bandwidth set by `density_bandwidth`
determines the bandwidth of the RBF kernel in KDE used to generate the plot.
Smaller means more affected by local changes. Set the colors and markers in the __init__ function
'''
if acc > sim.max_time:
acc = int(sim.max_time)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
# infections
r = restart
is_expo = self.__is_state_at(sim, r, 'expo', at_time)
is_iasy = self.__is_state_at(sim, r, 'iasy', at_time)
is_ipre = self.__is_state_at(sim, r, 'ipre', at_time)
is_isym = self.__is_state_at(sim, r, 'isym', at_time)
is_infected = is_iasy | is_ipre | is_isym
no_state = (1 - is_infected) & (1 - is_expo)
idx_expo = np.where(is_expo)[0]
idx_infected = np.where(is_infected)[0]
idx_none = np.where(no_state)[0]
# self.color_isym = 'red'
# self.color_expo= 'yellow'
### sites
site_loc = sim.site_loc
ax.scatter(site_loc[:, 0], site_loc[:, 1], alpha=self.filling_alpha, label='public sites',
marker=self.marker_site, color=self.color_site, facecolors=self.color_site, s=self.size_site)
### home locations and their states
home_loc = sim.home_loc
# no state
ax.scatter(home_loc[idx_none, 0], home_loc[idx_none, 1],
marker=self.marker_home, color=self.color_home,
facecolors='none', s=self.size_home)
try:
# expo
ax.scatter(home_loc[idx_expo, 0], home_loc[idx_expo, 1],
marker=self.marker_home, color=self.color_home,
facecolors=self.color_expo, s=self.size_home, label='exposed households')
sns.kdeplot(home_loc[idx_expo, 0], home_loc[idx_expo, 1], shade=True, alpha=self.density_alpha,
shade_lowest=False, cbar=False, ax=ax, color=self.color_expo, bw=density_bandwidth, zorder=0)
# infected
ax.scatter(home_loc[idx_infected, 0], home_loc[idx_infected, 1],
marker=self.marker_home, color=self.color_home,
facecolors=self.color_isym, s=self.size_home, label='infected households')
sns.kdeplot(home_loc[idx_infected, 0], home_loc[idx_infected, 1], shade=True, alpha=self.density_alpha,
shade_lowest=False, cbar=False, ax=ax, color=self.color_isym, bw=density_bandwidth, zorder=0)
except:
print('KDE failed, likely no exposed and infected at this time. Try different timing.')
plt.close()
return
# axis
ax.set_xlim((-0.1, 1.1))
ax.set_ylim((-0.1, 1.1))
plt.axis('off')
# legend
fig.legend(loc='center right', borderaxespad=0.1)
# Adjust the scaling factor to fit your legend text completely outside the plot
plt.subplots_adjust(right=0.85)
ax.set_title(title, pad=20)
plt.draw()
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def compare_hospitalizations_over_time(self, sims, titles, figtitle='Hospitalizations', filename='compare_hosp_0',
capacity_line_at=20, figsize=(10, 10), errorevery=20, acc=1000, ymax=None):
''''
Plots total hospitalizations for each simulation, named as provided by `titles`
to compare different measures/interventions taken. Colors taken as defined in __init__, and
averaged over random restarts, using error bars for std-dev.
The value of `capacity_line_at` defines the y-intercept of the hospitalization capacity line
'''
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
for i in range(len(sims)):
if acc > sims[i].max_time:
acc = sims[i].max_time
ts, line_hosp, error_sig = self.__comp_state_over_time(
sims[i], 'hosp', acc)
line_xaxis = np.zeros(ts.shape)
# lines
ax.errorbar(ts, line_hosp, yerr=error_sig, errorevery=errorevery,
c='black', linestyle='-', elinewidth=0.8)
# filling
ax.fill_between(ts, line_xaxis, line_hosp, alpha=self.filling_alpha, zorder=0,
label=r'Hospitalized under: ' + titles[i], edgecolor=self.color_different_scenarios[i],
facecolor=self.color_different_scenarios[i], linewidth=0)
# capacity line
ax.plot(ts, capacity_line_at * np.ones(ts.shape[0]), label=r'Max. hospitalization capacity',
c='red', linestyle='--', linewidth=4.0)
# axis
ax.set_xlim((0, np.max(ts)))
if ymax is None:
ymax = 1.5 * np.max(line_hosp + error_sig)
ax.set_ylim((0, ymax))
ax.set_xlabel(r'$t$ [days]')
ax.set_ylabel(r'[people]')
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
# legend
fig.legend(loc='center right', borderaxespad=0.1)
# Adjust the scaling factor to fit your legend text completely outside the plot
plt.subplots_adjust(right=0.70)
ax.set_title(figtitle, pad=20)
plt.draw()
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def plot_positives_vs_target(self, sim, targets, test_lag, title='Example',
filename='inference_0', figsize=(6, 5), errorevery=1, acc=17, ymax=None,
start_date='1970-01-01', lockdown_label='Lockdown', lockdown_at=None,
lockdown_label_y=None, subplot_adjust=None):
''''
Plots daily tested averaged over random restarts, using error bars for std-dev
together with targets from inference
'''
if acc > sim.max_time:
acc = int(sim.max_time)
fig, ax = plt.subplots(figsize=figsize)
# inference
ts, posi_mu, posi_sig = self.__comp_state_over_time(sim, 'posi', acc)
# shift by `test_lag` to count the cases on the real dates, as the real data does
T = posi_mu.shape[0]
corr_posi, corr_sig = np.zeros(T - test_lag), np.zeros(T - test_lag)
corr_posi[0 : T - test_lag] = posi_mu[test_lag : T]
corr_sig[0: T - test_lag] = posi_sig[test_lag: T]
# Convert x-axis into posix timestamps and use pandas to plot as dates
xx = days_to_datetime(ts[:T - test_lag], start_date=start_date)
ax.plot(xx, corr_posi, c='k', linestyle='-',
label='COVID-19 simulated case data')
ax.fill_between(xx, corr_posi-corr_sig, corr_posi+corr_sig,
color='grey', alpha=0.1, linewidth=0.0)
# target
# txx = np.linspace(0, targets.shape[0] - 1, num=targets.shape[0])
# # Convert x-axis into posix timestamps and use pandas to plot as dates
# txx = days_to_datetime(txx, start_date=start_date)
# ax.plot(txx, targets, linewidth=4, linestyle='', marker='X', ms=6,
# color='red', label='COVID-19 case data')
target_widget(targets, start_date, ax)
# axis
#ax.set_xlim((0, np.max(ts)))
if ymax is None:
ymax = 1.5 * np.max(posi_mu)
ax.set_ylim((0, ymax))
# ax.set_xlabel('Days')
ax.set_ylabel('Positive cases')
if lockdown_at is not None:
lockdown_widget(lockdown_at, start_date,
lockdown_label_y, ymax,
lockdown_label, ax)
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
#set ticks every week
ax.xaxis.set_major_locator(mdates.WeekdayLocator())
#set major ticks format
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
fig.autofmt_xdate(bottom=0.2, rotation=0, ha='center')
# legend
ax.legend(loc='upper left', borderaxespad=0.5)
subplot_adjust = subplot_adjust or {'bottom':0.14, 'top': 0.98, 'left': 0.12, 'right': 0.96}
plt.subplots_adjust(**subplot_adjust)
plt.draw()
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI)#, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def plot_daily_rts(self, sims, filename, start_date, titles=None, sigma=None,
r_t_range=R_T_RANGE, window=3, figsize=(6, 5),
subplot_adjust=None, lockdown_label='Lockdown',
lockdown_at=None, lockdown_label_y=None, ymax=None,
colors=['grey'], fill_between=True, draw_dots=True,
errorevery=1, show_legend=False, xtick_interval=1):
# If a single summary is provided
if not isinstance(sims, list):
sims = [sims]
sigma = [sigma]
results = list()
for i, sim in enumerate(sims):
res = compute_daily_rts(sim, start_date, sigma[i], r_t_range, window)
results.append(res)
# Colors
ABOVE = [1,0,0]
MIDDLE = [1,1,1]